Guangzhong Sun

T-drive: driving directions based on taxi trajectories

GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. In our approach, we propose a time-dependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest route. We build our system based on a real-world trajectory dataset generated by over 33,000 taxis in a period of 3 months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70% of the routes suggested by our method are faster than the competing methods, and 20% of the routes share the same results. On average, 50% of our routes are at least 20% faster than the competing approaches.

An Interactive-Voting Based Map Matching Algorithm

Matching a raw GPS trajectory to roads on a digital map is often referred to as the Map Matching problem. However, the occurrence of the low-sampling-rate trajectories (e.g. one point per 2 minutes) has brought lots of challenges to existing map matching algorithms. To address this problem, we propose an Interactive Voting-based Map Matching (IVMM) algorithm based on the following three insights: 1) The position context of a GPS point as well as the topological information of road networks, 2) the mutual influence between GPS points (i.e., the matching result of a point references the positions of its neighbors; in turn, when matching its neighbors, the position of this point will also be referenced), and 3) the strength of the mutual influence weighted by the distance between GPS points (i.e., the farther distance is the weaker influence exists). In this approach, we do not only consider the spatial and temporal information of a GPS trajectory but also devise a voting-based strategy to model the weighted mutual influences between GPS points. We evaluate our IVMM algorithm based on a user labeled real trajectory dataset. As a result, the IVMM algorithm outperforms the related method (ST-Matching algorithm).

Drive Smartly as a Taxi Driver

GPS-equipped taxis are mobile sensors probing the traffic flow on road surfaces, and taxi drivers are experienced drivers who can usually find out the fastest path to a destination based on their knowledge. In this demo, we provide a user with the practically fastest path to a destination at a given departure time in terms of taxi drivers’ intelligence mined from historical GPS trajectories of taxis. We build our system, called T-Drive, by using a real trajectory dataset generated by over 33,000 taxis in a period of 3 months, and conduct both synthetic experiments and in-the-field evaluations. As a result, our method outperforms the real-time-traffic-based (RT) and the speed-constraint-based (SC) approaches in both efficiency and effectiveness.

Protecting Privacy in Location-Based Services Using K-Anonymity without Cloaked Region

The emerging location-detection devices together with ubiquitous connectivity have enabled a large variety of location-based services (LBS). Unfortunately, LBS may threaten the users’ privacy. K-anonymity cloaking the user location to K-anonymizing spatial region (K-ASR) has been extensively studied to protect privacy in LBS. Traditional K-anonymity method needs complex query processing algorithms at the server side. SpaceTwist [8] rectifies the above shortcoming of traditional K-anonymity since it only requires incremental nearest neighbor (INN) queries processing techniques at the server side. However, Space Twist may fail since it cannot guarantee K-anonymity. In this paper, our proposed framework, called KAWCR (K-anonymity Without Cloaked Region), rectifies the shortcomings and retains the advantages of the above two techniques. KAWCR only needs the server to process INN queries and can guarantee that the users issuing the query is indistinguishable from at least K-1 other users. The extensive experimental results show that the communication cost of KAWCR for kNN queries is lower than that of both traditional K-anonymity and SpaceTwist.

A New Progressive Algorithm for a Multiple Longest Common Subsequences Problem and Its Efficient Parallelization

The multiple longest common subsequence (MLCS) problem, which is related to the measurement of sequence similarity, is one of the fundamental problems in many fields. As an NP-hard problem, finding a good approximate solution within a reasonable time is important for solving large-size problems in practice. In this paper, we present a new progressive algorithm, Pro-MLCS, based on the dominant point approach. Pro-MLCS can find an approximate solution quickly and then progressively generate better solutions until obtaining the optimal one. Pro-MLCS employs three new techniques: 1) a new heuristic function for prioritizing candidate points; 2) a novel d-index-tree data structure for efficient computation of dominant points; and 3) a new pruning method using an upper bound function and approximate solutions. Experimental results show that Pro-MLCS can obtain the first approximate solution almost instantly and needs only a very small fraction, e.g., 3 percent, of the entire running time to get the optimal solution. Compared to existing state-of-the-art algorithms, Pro-MLCS can find better solutions in much shorter time, one to two orders of magnitude faster. In addition, two parallel versions of Pro-MLCS are developed: DPro-MLCS for distributed memory architecture and DSDPro-MLCS for hierarchical distributed shared memory architecture. Both parallel algorithms can efficiently utilize parallel computing resources and achieve nearly linear speedups. They also have a desirable progressiveness property-finding better solutions in shorter time when given more hardware resources.

Study on Parallel Machine Scheduling Problem with Buffer

In the papers by Kellerer et al. (1995) and Zhang (1997) the authors investigate a semi on-line version of a classical parallel machine scheduling problem. In the semi on-line version, there is a buffer of length k which is available to maintain k jobs. The jobs arrive one by one and can be temporarily assigned to the buffer if the buffer is not full. The goal is to assign all jobs to m identical machines such that the makespan is minimized. For two machines, Kellerer et al. (1995) presented a lower bound 4/3 of the worst-case ratio and Zhang (1997) presented an optimal algorithm with a buffer of length 1. In this paper, we investigate the problem with m ges 2 machines. First, we design a new algorithm for Pm//Cmax with a buffer of length lfloorm/2rfloor and analyze it. Second, we present some lower bounds of the worst-case ratio.

CCCFNet: A Content-Boosted Collaborative Filtering Neural Network for Cross Domain Recommender Systems

To overcome data sparsity problem, we propose a cross domain recommendation system named CCCFNet which can combine collaborative filtering and content-based filtering in a unified framework. We first introduce a factorization framework to tie CF and content-based filtering together. Then we find that the MAP estimation of this framework can be embedded into a multi-view neural network. Through this neural network embedding the framework can be further extended by advanced deep learning techniques.

Robust Spammer Detection in Microblogs: Leveraging User Carefulness

Microblogging Web sites, such as Twitter and Sina Weibo, have become popular platforms for socializing and sharing information in recent years. Spammers have also discovered this new opportunity to unfairly overpower normal users with unsolicited content, namely social spams. Although it is intuitive for everyone to follow legitimate users, recent studies show that both legitimate users and spammers follow spammers for different reasons. Evidence of users seeking spammers on purpose is also observed. We regard this behavior as useful information for spammer detection. In this article, we approach the problem of spammer detection by leveraging the “carefulness” of users, which indicates how careful a user is when she is about to follow a potential spammer. We propose a framework to measure the carefulness and develop a supervised learning algorithm to estimate it based on known spammers and legitimate users. We illustrate how the robustness of the detection algorithms can be improved with aid of the proposed measure. Evaluation on two real datasets from Sina Weibo and Twitter with millions of users are performed, as well as an online test on Sina Weibo. The results show that our approach indeed captures the carefulness, and it is effective for detecting spammers. In addition, we find that our measure is also beneficial for other applications, such as link prediction.

SPLZ: An efficient algorithm for single source shortest path problem using compression method

Efficient solution of the single source shortest path (SSSP) problem on road networks is an important requirement for numerous real-world applications. This paper introduces an algorithm for the SSSP problem using compression method. Owning to precomputing and storing all-pairs shortest path (APSP), the process of solving SSSP problem is a simple lookup of a little data from precomputed APSP and decompression. APSP without compression needs at least 1TB memory for a road network with one million vertices. Our algorithm can compress such an APSP into several GB, and ensure a good performance of decompression. In our experiment on a dataset about Northwest USA (with 1.2 millions vertices), our method can achieve about three orders of magnitude faster than Dijkstra algorithm based on binary heap.

A Novel Dummy-Based Mechanism to Protect Privacy on Trajectories

In recent years, wireless communication technologies and accurate positioning devices enable us to enjoy various types of location-based services. However, revealing users location information to potentially untrusted LBS providers is one of the most significant privacy threats in location-based services. The dummy-based privacy-preserving approach is a popular technology that can protect real trajectories from exposing to attackers. Moreover, it does not need a trusted third part, while guaranteeing the quality of service. When user requests a service, dummy trajectories anony mize the real trajectory to satisfy privacy-preserving requirements. In this paper, we propose a new privacy model that includes three reasonable privacy metrics. We also design a new algorithm named adaptive dummy trajectories generation algorithm (ADTGA) to derive uniformly distributed dummy trajectories. Dummy trajectories generated by our algorithm can achieve stricter privacy-preserving requirements based on our privacy model. The experimental results show that our proposed algorithm can use fewer dummy trajectories to satisfy the same privacy-preserving requirement than existing algorithms, and the distribution of dummy trajectories is more uniformly.